1. Field of the Invention
The present invention relates to an apparatus and a method for detecting a change in status of an object to be monitored.
2. Description of the Related Art
Conventionally, a technique of continuously monitoring and analyzing the behavior of a communication system or various facilities has been studied to detect an abnormal condition or its foretaste. In this type of analysis, it is necessary to set various thresholds in advance for each parameter depending on the characteristic of monitor data. However, it is not easy to determine in advance an appropriate threshold.
On the other hand, the patent document 1 (Japanese Published Patent Application No. 2005-285040) describes a method of dynamically generating a threshold for determination of the status of an object to be monitored according to the statistics. However, it is necessary in this method that the following two prerequisites are satisfied.
Condition 1: The data to be processed is random (non-correlated) data.
Condition 2: The data has a specific distribution.
However, when a change of the status of an object to be monitored is detected, it is necessary to collect and analyze the measurement data about the object to be monitored in a time series. However, the time-series data is not random data because the time-series data has a correlation with the past data. Additionally, obtained data does not necessarily have a specific distribution. Therefore, it is difficult to apply the statistical method to the time-series data without modification.
To solve the problem, there is a method to detect a change of time-series data by considering that the modeling error on the time-series data is random and indicates white noise based on the normal distribution, and applying a statistical test to the white noise (for example, the patent document 2 (Japanese Published Patent Application No. 2005-157579)). The method described in the patent document 2 is described as follows.
A Kalman filter is applied to the data acquired from an object to be monitored. When the residual sequence Y(t) between the estimated value of the data by the Kalman filter and the acquired data is normal, the residual sequence Y(t) is a normal white noise vector having a mean value of zero and the covariance matrix is V(t). Therefore, the covariance matrix V(t) of the white noise vector in a normal state is obtained in advance, and a whiteness test is conducted on the residual sequence Y(t) according to χ2 test, thereby determining whether or not a system is normal.
Thus, according to the method described in the patent document 2, an abnormal condition of an object to be monitored can be detected, although the time-series data not satisfying the above-mentioned conditions 1 and 2 is monitored.
However, the Kalman filter used in the method described in the patent document 2 requires complex calculation and it is hard to monitor a large-scale system in real time. In the method described in the patent document 2, it is necessary to hold the past data sequence. Therefore, large memory is required. Furthermore, in this system, it is necessary to prepare a reference distribution in advance.